Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the burden of behavioral disclosure. It thoroughly describes the return behavior: an array of creatives ranked by store visits, including nested fields like attention metrics, bestContext, and dateRange. It does not mention error conditions, permissions, or side effects, but as a read-only 'get' operation, the main behavior is well covered.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and front-loaded with a one-sentence summary followed by focused sections. Every line adds value: the purpose statement, three concrete use cases, and a detailed return format list. There is no filler or redundancy, making it appropriately concise despite its length.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has only one parameter and no output schema, the description compensates completely by detailing the return structure with nested objects (attention, bestContext, dateRange) and ranking semantics. Combined with clear use cases, it gives an agent all necessary context to select and invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% descriptive coverage for campaign_id ('Campaign identifier'), and the description does not add extra semantic detail for parameters. According to the baseline rule for high schema coverage, a score of 3 is appropriate—the schema already documents the parameter adequately.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get attribution performance by individual creative variant.' It uses a specific verb ('Get') and resource ('creative variant'), and uniquely distinguishes itself from sibling tools like get_campaign_attribution and get_attribution_timeseries by focusing on per-creative performance and providing a concrete outcome ('which creative variant drove the most store visits?').

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

A dedicated 'WHEN TO USE' section lists three specific scenarios, such as comparing A/B/C creative tests and finding optimal creative x context combinations. While it does not explicitly name alternatives or exclusions, the use cases are clear and actionable. This is more than implied usage but stops short of explicitly contrasting with sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.